Voice Memo Organizer

Description

--- name: voice-memo-organizer description: Find, transcribe, summarize, and organize all Apple Voice Memos into a searchable archive. Batch processes hundreds of recordings locally using whisper.cpp — no API keys needed. --- # Voice Memo Organizer Turn hundreds of untitled Apple Voice Memos into a searchable, organized archive with descriptive titles, summaries, themes, and key quotes. ## When to Use - User wants to organize their voice memos - User mentions having a lot of recordings they can't find or search - User wants to transcribe voice memos in bulk - User asks about finding Apple Voice Memos files on macOS ## What It Does 1. **Finds** all Apple Voice Memos on macOS (they're hidden in a non-obvious path) 2. **Extracts** metadata from the SQLite database (dates, durations, labels) 3. **Transcribes** every recording locally using whisper.cpp (no API keys needed) 4. **Summarizes** each transcript with a descriptive title, themes, key quotes, and type 5. **Builds** a searchable master index document ## Prerequisites - macOS with Voice Memos app (recordings synced via iCloud from iPhone) - **Full Disk Access** for Terminal: System Settings > Privacy & Security > Full Disk Access > enable your terminal app - [Homebrew](https://brew.sh) — if not installed: `/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"` ## Step-by-Step Process ### Step 1: Access Voice Memos Voice Memos are stored at: ``` ~/Library/Group Containers/group.com.apple.VoiceMemos.shared/Recordings/ ``` This path requires Full Disk Access. Verify access: ```bash ls ~/Library/Group\ Containers/group.com.apple.VoiceMemos.shared/Recordings/ | head -5 ``` If you get "Operation not permitted", the user needs to enable Full Disk Access for Terminal in System Settings > Privacy & Security > Full Disk Access. If the directory doesn't exist or is empty, the user may need to open the Voice Memos app on their Mac first and wait for iCloud to sync their recordings. The folder also contains `CloudRecordings.db` — a SQLite database with metadata: ```bash sqlite3 ~/Library/Group\ Containers/group.com.apple.VoiceMemos.shared/Recordings/CloudRecordings.db \ "SELECT COUNT(*), ROUND(SUM(ZDURATION)/3600,1) as hours FROM ZCLOUDRECORDING WHERE ZPATH IS NOT NULL;" ``` ### Step 2: Copy Recordings & Export Metadata ```bash mkdir -p ~/Documents/Voice-Memos-Organized/{transcripts,summaries,models} mkdir -p ~/Documents/Voice-Memos-Raw # Copy all recordings cp ~/Library/Group\ Containers/group.com.apple.VoiceMemos.shared/Recordings/*.m4a ~/Documents/Voice-Memos-Raw/ 2>/dev/null cp ~/Library/Group\ Containers/group.com.apple.VoiceMemos.shared/Recordings/*.qta ~/Documents/Voice-Memos-Raw/ 2>/dev/null # Export metadata sqlite3 -header -csv ~/Library/Group\ Containers/group.com.apple.VoiceMemos.shared/Recordings/CloudRecordings.db \ "SELECT Z_PK as id, ZCUSTOMLABEL as label, ZPATH as filename, ROUND(ZDURATION,1) as duration_secs, datetime(ZDATE + 978307200, 'unixepoch', 'localtime') as recorded_date FROM ZCLOUDRECORDING WHERE ZPATH IS NOT NULL AND ZPATH != '' ORDER BY ZDATE ASC;" \ > ~/Documents/Voice-Memos-Organized/recordings-metadata.csv ``` ### Step 3: Install Transcription Tools ```bash # Install ffmpeg for audio conversion brew install ffmpeg # Download whisper.cpp base.en model (~150MB, good speed/accuracy for English) curl -L -o ~/Documents/Voice-Memos-Organized/models/ggml-base.en.bin \ "https://huggingface.co/ggerganov/whisper.cpp/resolve/main/ggml-base.en.bin" ``` For the whisper binary: ```bash # Install whisper.cpp via brew (recommended) brew install whisper-cpp # The binary is called whisper-cli (not whisper-cpp): which whisper-cli # /opt/homebrew/bin/whisper-cli (Apple Silicon) or /usr/local/bin/whisper-cli (Intel) ``` ### Step 4: Batch Transcribe For each audio file: 1. Convert to 16kHz mono WAV using ffmpeg 2. Run whisper.cpp with the base.en model 3. Save transcript as .txt ```bash WHISPER="$(command -v whisper-cli)" # auto-detect path MODEL="$HOME/Documents/Voice-Memos-Organized/models/ggml-base.en.bin" for audiofile in ~/Documents/Voice-Memos-Raw/*; do [ -f "$audiofile" ] || continue BASENAME=$(basename "$audiofile" | sed 's/\.[^.]*$//') TEMP_WAV="/tmp/vm_${BASENAME}.wav" # Skip if already transcribed [ -s ~/Documents/Voice-Memos-Organized/transcripts/"${BASENAME}.txt" ] && continue # Convert to WAV (skip on failure to avoid stale data) if ! ffmpeg -y -i "$audiofile" -ar 16000 -ac 1 -c:a pcm_s16le "$TEMP_WAV" 2>/dev/null; then echo "SKIP: Could not convert $BASENAME" continue fi # Transcribe "$WHISPER" -m "$MODEL" -f "$TEMP_WAV" --output-txt \ -of ~/Documents/Voice-Memos-Organized/transcripts/"$BASENAME" -t 8 --no-timestamps 2>/dev/null rm -f "$TEMP_WAV" done ``` **Performance note:** ~1 minute of audio transcribes in ~1 second on Apple Silicon. 67 hours takes roughly 1 hour. ### Step 5: Summarize Each Transcript For each transcript, generate a JSON summary with: - **title**: Short descriptive title (5-10 words) - **summary**: 2-3 sentence summary of the content - **themes**: Array of tags (e.g. "business", "personal", "health", "brainstorm") - **key_quotes**: Array of 1-3 notable verbatim quotes - **type**: One of "conversation", "brainstorm", "voice_note", "meeting", "personal", "phone_call" Save each as a JSON file in the summaries/ folder. Process in batches of 10-20 transcripts at a time using parallel agents for speed. Skip transcripts that are too short (<10 words) or contain only silence/noise. ### Step 6: Build Master Index Create `voice-memos-master-index.md` with: - Stats header (count, date range, total hours) - Theme breakdown - Type breakdown - Chronological entries with: date, title, duration, type, themes, summary, key quotes, filename - "Best Quotes" section at the end ## Output Structure ``` ~/Documents/Voice-Memos-Organized/ ├── voice-memos-master-index.md ← searchable master document ├── transcripts/ ← full text of every recording ├── summaries/ ← JSON summaries with titles/themes/quotes ├── recordings-metadata.csv ← database export └── models/ ← whisper model ~/Documents/Voice-Memos-Raw/ ← original audio files ``` ## Tips - The master index works great in Obsidian, VS Code, or any text editor with Cmd+F search - For non-English memos, use `ggml-base.bin` (multilingual) instead of `ggml-base.en.bin` - For higher accuracy on important recordings, use `ggml-medium.en.bin` (~500MB) - The SQLite database also contains folder info if the user organized memos into folders in the app

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